Bras. Political Sci. Rev.2015;9(3):178-85.
Causal Inference, Shaolin Style: “Mastering ’Metrics”
(Angrist, Joshua D. and Pischke, Jörn-Steffen. Mastering ‘Metrics: The Path From Cause to Effect. Princeton University Press, 2014)
The field of causal inference has seen a remarkable development in the last few years. While social scientists have always devoted considerable effort to understand causal effects, their quest for the perfect identification strategy received a new impulse after the publication of Donald Rubin’s causal model in the late 1970s (; . Although the “potential outcomes revolution” is still in its infancy, a myriad of statistical methods have been designed to help researchers untangle true relationships from spurious effects (. However, such methods already have an established place in cutting-edge publications. They remain relatively unknown to undergraduate students of economics and to academics in other areas.